The agricultural community and decision makers require tools to reliably predict crop yield, to assess optimal management
practices and economic impacts. UVMRP is currently developing the Climate-Agroecosystem-UV
Interactions and Economic (CAIE) system with collaborators
from the University of Maryland and Colorado State University. CAIE system that
will couple an advanced regional climate model with models of crops and ecosystems
and economics, integrating the data from the monitoring network, the results of the
effects studies, and satellite observations. This system will allow studies on how
climate and crop production interact and how the interaction impacts economics and
management practices.

CAIE is an integrated assessment system that simulates climate,
UV radiation, crop growth, and economics. It will be able to predict how crop
yield and quality will respond to changes in environmental including temperature,
moisture (drought), nutrients, UV-B radiation, CO2 concentration, aerosols and other
air pollutants. It will be capable of simulating agriculturally important crops
(such as cotton, corn, soybean, wheat, and rice), forests, and rangelands.
CAIE will include an economic assessment model that predicts economic growth of
crop agriculture (total factor productivity change) based on climate. The system will
have the ability analyze policy, land use, and management practices. Ultimately,
the system will provide the science support for U.S. policy makers to not only
establish necessary incentives and safety nets for producers, but also to assess
potential risks, determine optimal practices, design effective policies, and identify
mitigation and adaptation strategies to achieve sustainable development of agriculture.

Climate-Agroecosystem-UV Interactions and Economic system

CAIE has the following components:

CWRF (Climate-Weather Research and Forecasting model) is a state of the art model that comprehensively simulates the processes behind regional climate and weather. CWRF runs on horizontal grids at U.S. nationwide scale and is capable of simulating terrestrial hydrology, precipitation, and radiation, all of which are critical for crop growth.

LDAS (NASA Land Data Assimilation System) assimilates best available observations to produce spatially and temporally consistent land-surface model datasets, intended to reduce the errors in key variables of climate and weather models.

DSSAT (Decision Support System for Agrotechnology Transfer) is a crop model capable of simulating many species individually, based on the genetic characteristics of each species.

GOSSYM (a shortened scientific name for the genus of cotton, Gossypium) is a mechanistic model developed by USDA to simulate cotton growth given soil, weather, and management practices.

DayCent-UV (Daily version of Century model with UV module) is a modified version of a widely used terrestrial ecosystem biogeochemistry model DayCent, which simulates photosynthesis, plant production, carbon allocation, autotrophic and heterotrophic respiration, decomposition, evaporation, transpiration, phenology, disturbances such as fire and grazing, and management practices such as fertilizer use and irrigation.

TUV (Tropospheric Ultraviolet and Visible radiation model), is a well-tested radiation transfer model developed by National Center for Atmospheric Research.

FASOMGHG (Forest and Agricultural Sector Optimization Model – GreenHouse Gasses version) is an economic model used by EPA that simulates land allocation and the economic impacts of changing land allocation and production practices.

For more information on ongoing research, select an interest below.

CWRF

Description

The heart of CAIE system is a climate-crop model that simulates crop growth and yield based on weather, soil, and crop physiology.
The climate-crop model is centered on a state of the art Climate-Weather Research and
Forecasting model (CWRF) that simulates the processes behind regional climate and
weather at a national scale (Liang et al 2012a). It is capable of simulating terrestrial hydrology,
precipitation, and solar radiation that are all critical for crop growth. WRF has Conjunctive Surface-Subsurface Process model
(CSSP) that simulates plant canopy properties, soil temperature/moisture distributions, terrestrial hydrology variations, and
land-atmosphere exchanges of water, heat, and moment fluxes. In the climate-crop model, CWRF is coupled multiple models of crops by
passing information through CSSP. One of the crop model is GOSSYM (a shortened scientific name for the genus of cotton, Gossypium),
a mechanistic model for cotton developed by USDA to simulate cotton growth given soil, weather, and management practices.

UVMRP has coupled the climate model CWRF with the cotton growth model GOSSYM, and compared the results against reported yield
(Liang et al 2012b, Liang et al 2012c). The results matched the reported yield well, especially how the yield varied in time and space.
GOSSYM was modified to incorporate our results from studies on effects of UV-B radiation on cotton growth.
The coupled model has the ability to simulate cotton growth and productivity under changing environmental stress factors such as
extreme temperatures, drought, CO2, and solar UV-B radiation.

UVMRP is in a process of coupling CWRF with another crop model DSSAT-corn.
This requires extensive modifications and recoding of DSSAT-corn, and we are in the process of
doing so. Once coupled, they will be tested by retrospectively simulating crop yields over
various U.S. regions with historical climate data. We will compare the simulations with both
observational data and model simulations from other studies to attribute crop yields to key
environmental factors and stressors, including temperature, precipitation, soil moisture, UV-B
radiation, and CO2 fertilization.

The climate-crop model must accurately simulate UV-B radiation that the plants experience, and
UVMRP will incorporate the models that simulate UV-B radiation passes through the atmosphere
(TUV) and through plant canopies (CUV). TUV (Tropospheric Ultraviolet and Visible radiation model)
is a well-tested model developed by National Center for Atmospheric Research, and CUV (3-D Canopy UV radiation transfer model)
is a model that predicts UV-B radiation within and below a plant canopy. Once integrated into CWRF, TUV and CUV will be able
integrate near-real time meteorological conditions and NASA satellite assimilated data to retrieve UV-B radiation covering the
entire U.S. from 1979 onward. We will test them against UVMRP data.

DayCent-UV

Description

UVMRP will develop an UV version of DayCent. DayCent is a well-tested model that simulates variety of ecosystems including
grasslands of western U.S. that receives high doses of UV-B radiation. In such places UV-B radiation and visible light can
accelerate litter decay in a process called photodegradation, possibly releasing more nutrients from the litter to be taken up
by plants or be lost from the system through runoff. We will develop an UV-B module in the DayCent model to
incorporate photodegradation. The DayCent-UV model will be calibrated and validated against the experimental data from publications,
10-year litter decay data, and other observed ecosystem variables at multiple western U.S. sites.

UVMRP will couple DayCent-UV with CWRF to examine the effects of UV-B on ecosystems and how they
in turn impact climate. The procedure here will be similar to those for DSSAT. We will extensively
modify and recode DayCent-UV to be compatible with CWRF.
We will also develop a method to incorporate management strategies from national inventories.

Economic Model

Description

Policy makers and decision makers in agriculture must evaluate the economic consequence of agricultural production under
climate change at the regional or national level. UVMRP will thus incorporate into CAIE an economic model FASOMGHG
(Forest and Agricultural Sector Optimization Model – Green House Gasses version) used by EPA to simulate land allocation and the
economic impacts of changing land allocation and production practices. FASOMGHG simulates the biophysical and economic processes
that determine technical, economic, and environmental implications of bioenergy production, climate change and policy intervention.
We will expand it to reflect municipal and
energy sector demands for water, considering aquifer depth, pumping cost, wind energy, and solar power.

The economic dynamics also reflect the changes in resource allocation and energy and land use at relatively longer time and
spatial scales than represented in a regional model. We will thus develop a feasible approach that links CWRF-CROP predicted
climate and crop yield distributions at the county level to the statistical economy model of aggregate agricultural productivity
at the national level. We will look for the correlation between total factor productivity change (TFPC) and key climate indices and
gain the physical understanding of their relationships. We will then develop a multivariate model for TFPC and climate indices to
predict TFP growth, and apply this regression model to regional climate changes projected by the CWRF-CROP model to predict the
potential trends of future U.S. agricultural TFP. The outcome of this research will lead to an interdisciplinary approach for
developing an integrated system model infrastructure CAIE to achieve a credible and quantitative assessment of key stress factors,
climate feedbacks and economic impacts for U.S. agriculture,
and consequently predict the likely changes in agricultural productivity in a changing climate.